{% load static %}

EDA - 탐색적 데이터 분석

호텔 예약 수요를 분석해보자

• city hotel, resort hotel의 예약 정보들을 이용해서 호텔 예약 수요를 예측한다
• data : 예약 일자, 숙박 기간, 방문자 유형별 인원, 주차 공간 등등

1. 분석의 목적과 변수 확인

• 호텔을 예약하기 좋은 시기는 언제일까?
• 투숙 기간은 어느정도가 좋을까?
• 누가 예약을 취소할까?

# 라이브러리 불러오기
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pi
import seaborn as sns

# 데이터 불러오기

# hb_df = pd.read_csv("./csv/hotel_bookings.csv")
full_data = pd.read_csv("./hotel_bookings.csv")

데이터 정보, missing value 확인

full_data.head()

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	0	342	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
1	Resort Hotel	0	737	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
2	Resort Hotel	0	7	2015	July	27	1	0	1	1	...	No Deposit	NaN	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
3	Resort Hotel	0	13	2015	July	27	1	0	1	1	...	No Deposit	304.0	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
4	Resort Hotel	0	14	2015	July	27	1	0	2	2	...	No Deposit	240.0	NaN	0	Transient	98.0	0	1	Check-Out	2015-07-03

5 rows × 32 columns

# check for missing values
full_data.isnull().sum()

hotel                                  0
is_canceled                            0
lead_time                              0
arrival_date_year                      0
arrival_date_month                     0
arrival_date_week_number               0
arrival_date_day_of_month              0
stays_in_weekend_nights                0
stays_in_week_nights                   0
adults                                 0
children                               4
babies                                 0
meal                                   0
country                              488
market_segment                         0
distribution_channel                   0
is_repeated_guest                      0
previous_cancellations                 0
previous_bookings_not_canceled         0
reserved_room_type                     0
assigned_room_type                     0
booking_changes                        0
deposit_type                           0
agent                              16340
company                           112593
days_in_waiting_list                   0
customer_type                          0
adr                                    0
required_car_parking_spaces            0
total_of_special_requests              0
reservation_status                     0
reservation_status_date                0
dtype: int64

data cleaning

nan_replacements = {"children:": 0.0,"country": "Unknown", "agent": 0, "company": 0}
full_data_cln = full_data.fillna(nan_replacements)

투숙객의 국가 정보

# get number of acutal guests by country
country_data = pd.DataFrame(full_data_cln.loc[full_data_cln["is_canceled"] == 0]["country"].value_counts())
#country_data.index.name = "country"
country_data.rename(columns={"country": "Number of Guests"}, inplace=True)
total_guests = country_data["Number of Guests"].sum()
country_data["Guests in %"] = round(country_data["Number of Guests"] / total_guests * 100, 2)
country_data["country"] = country_data.index
#country_data.loc[country_data["Guests in %"] < 2, "country"] = "Other"

# pie plot
fig = px.pie(country_data,
             values="Number of Guests",
             names="country",
             title="Home country of guests",
             template="seaborn")
fig.update_traces(textposition="inside", textinfo="value+percent+label")
fig.show()

Room Type 과 Price의 box plot

# normalize price per night (adr):
full_data_cln["adr_pp"] = full_data_cln["adr"] / (full_data_cln["adults"] + full_data_cln["children"])
full_data_guests = full_data_cln.loc[full_data_cln["is_canceled"] == 0] # only actual gusts
room_prices = full_data_guests[["hotel", "reserved_room_type", "adr_pp"]].sort_values("reserved_room_type")

plt.figure(figsize=(12, 8))
sns.boxplot(x="reserved_room_type",
            y="adr_pp",
            hue="hotel",
            data=room_prices, 
            hue_order=["City Hotel", "Resort Hotel"],
            fliersize=0)
plt.title("Price of room types per night and person", fontsize=16)
plt.xlabel("Room type", fontsize=16)
plt.ylabel("Price [EUR]", fontsize=16)
plt.legend(loc="upper right")
plt.ylim(0, 160)
plt.show()

연간 Room Price

# grab data:
room_prices_mothly = full_data_guests[["hotel", "arrival_date_month", "adr_pp"]].sort_values("arrival_date_month")

# order by month:
ordered_months = ["January", "February", "March", "April", "May", "June", 
          "July", "August", "September", "October", "November", "December"]
room_prices_mothly["arrival_date_month"] = pd.Categorical(room_prices_mothly["arrival_date_month"], categories=ordered_months, ordered=True)

# barplot with standard deviation:
plt.figure(figsize=(12, 8))
sns.lineplot(x = "arrival_date_month", y="adr_pp", hue="hotel", data=room_prices_mothly, 
            hue_order = ["City Hotel", "Resort Hotel"], ci="sd", size="hotel", sizes=(2.5, 2.5))
plt.title("Room price per night and person over the year", fontsize=16)
plt.xlabel("Month", fontsize=16)
plt.xticks(rotation=45)
plt.ylabel("Price [EUR]", fontsize=16)
plt.show()

월간 평균 방문자 수

rh = full_data_cln.loc[(full_data_cln["hotel"] == "Resort Hotel") & (full_data_cln["is_canceled"] == 0)]
ch = full_data_cln.loc[(full_data_cln["hotel"] == "City Hotel") & (full_data_cln["is_canceled"] == 0)]

# Create a DateFrame with the relevant data:
resort_guests_monthly = rh.groupby("arrival_date_month")["hotel"].count()
city_guests_monthly = ch.groupby("arrival_date_month")["hotel"].count()

resort_guest_data = pd.DataFrame({"month": list(resort_guests_monthly.index),
                    "hotel": "Resort hotel", 
                    "guests": list(resort_guests_monthly.values)})

city_guest_data = pd.DataFrame({"month": list(city_guests_monthly.index),
                    "hotel": "City hotel", 
                    "guests": list(city_guests_monthly.values)})
full_guest_data = pd.concat([resort_guest_data,city_guest_data], ignore_index=True)

# order by month:
ordered_months = ["January", "February", "March", "April", "May", "June", 
          "July", "August", "September", "October", "November", "December"]
full_guest_data["month"] = pd.Categorical(full_guest_data["month"], categories=ordered_months, ordered=True)

# Dataset contains July and August date from 3 years, the other month from 2 years. Normalize data:
full_guest_data.loc[(full_guest_data["month"] == "July") | (full_guest_data["month"] == "August"),
                    "guests"] /= 3
full_guest_data.loc[~((full_guest_data["month"] == "July") | (full_guest_data["month"] == "August")),
                    "guests"] /= 2

#show figure:
plt.figure(figsize=(12, 8))
sns.lineplot(x = "month", y="guests", hue="hotel", data=full_guest_data, 
             hue_order = ["City hotel", "Resort hotel"], size="hotel", sizes=(2.5, 2.5))
plt.title("Average number of hotel guests per month", fontsize=16)
plt.xlabel("Month", fontsize=16)
plt.xticks(rotation=45)
plt.ylabel("Number of guests", fontsize=16)
plt.show()